Mining of remote sensing image archives using spatial relationship histograms
Date
2008-07Source Title
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2008
Publisher
IEEE
Pages
III - 589 - III - 592
Language
English
Type
Conference PaperItem Usage Stats
141
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Abstract
We describe a new image representation using spatial relationship histograms that extend our earlier work on modeling image content using attributed relational graphs. These histograms are constructed by classifying the regions in an image, computing the topological and distance-based spatial relationships between these regions, and counting the number of times different groups of regions are observed in the image. We also describe a selection algorithm that produces very compact representations by identifying the distinguishing region groups that are frequently found in a particular class of scenes but rarely exist in others. Experiments using Ikonos scenes illustrate the effectiveness of the proposed representation in retrieval of images containing complex types of scenes such as dense and sparse urban areas. © 2008 IEEE.
Keywords
Feature selectionImage retrieval
Spatial relationships
Attributed relational graph
Compact representation
Distance-based
Feature selection
Image content
Image representations
Remote sensing images
Selection algorithm
Spatial relationships
Urban areas
Feature extraction
Image reconstruction
Mining
Remote sensing
Image retrieval